Color-Correcting Product Photo Enhancer

@sanarena

Hi, I’m Armin Nikdel. I’d build this as a lightweight Python/OpenCV script with Pillow/imageio for JPEG/PNG I/O: detect colour cast from neutral regions and image statistics, apply white-balance/illumination correction in LAB/RGB, then preserve original resolution without aggressive denoising or contrast changes that can create artefacts. For batch mode, I’d add folder input/output handling, parameter validation, timing per image, and an exportable CSV log of processed files and any failures. I’ll test it on generic product images, include 2-3 before/after samples, and keep the code clean enough to embed later. INR 20500 and around 10 days works for this scope. Do you have any sample product photos with a known correct reference colour, or should I tune the first version against visual side-by-side accuracy on generic product sets?

@vishal1145

.

@muneeb6767

I have experience building Python/OpenCV image-processing pipelines for color correction, batch automation, and image enhancement, and can deliver a fast, well-documented solution that accurately corrects white balance and color casts while preserving image quality and original resolution.

@ShubhamKumarVfx

Hello There, ✅✅(I am Photoshop Expert)✅✅ COMPLETE THIS WITHIN 12 HOURS I am ready now to start working with you. I am highly experienced for this project. "check my Freelancer Portfolio" ✦✦**Portfolio**✦✦ https://www.freelancer.com/u/ShubhamKumarVfx Thanks and regards, Shubham Kumar VFX

@KrisuTechkul

Hi Krishna here from Delhi, founder of an AI and software development company with a focus on delivering tailor-made solutions that drive real-world impact - and your product could not be more aligned with our expertise. We have an established track record in developing advanced artificial intelligence systems, particularly in the areas of computer vision and color science, which will be crucial for successfully completing this project. We specialize in deploying machine learning algorithms, such as the ones you require to detect and correct color cast in product photos. Our proficiency in Python and OpenCV stack combined with our knowledge of ICC profiles and camera RAW workflows makes us well-suited to this task. Our algorithms not only accurately detect the colour cast but also ensure a true-to-life correction without introducing any artefacts or noise. Choose us for this project if you want unbeatable results paired with unparalleled dedication. Let us harness our AI prowess to enhance your product by correcting its photo colours, allowing it to look true-to-life on any screen or platform. I genuinely look forward to collaborating with you on this exciting endeavor!

@Hrolgar

Grey-world white balance assumes the average colour in your scene is neutral grey. For most product photography, it isn't, and that's what causes corrections to pull the wrong way on a big chunk of the batch. LAB colour space plus histogram matching against a reference image sidesteps that assumption entirely. The reference can be a calibration card shot or just a known-good image from your set. Single pass per image, one function call. Based on typical 4MP JPEG processing with OpenCV on a standard CPU, the 5-second target should be comfortable with room to spare. What you'd get: a Python module with a clean importable API, a CLI batch runner for directory-level runs, before/after samples from your images, and a usage doc covering the main edge cases. Three days, 31,875 INR, one milestone. Does the batch have calibration shots in it, or should I build in auto-detection for neutral patches?

@rahuldograarts

Leveraging my extensive experience in creative content production and expertise in Photoshop, I am well-positioned to deliver a Color-Correcting Product Photo Enhancer that exceeds your expectations. For over 15 years, I have honed my skills in digital painting, graphic design, and branding, all of which are essential for a project of this nature. Transitioning these skills into AI-Powered Video Production has allowed me to develop a unique perspective on utilizing technology for creating high-quality content without compromising the artistic integrity; a skill that will be invaluable for color correction while retaining the original look and feel of your product photos. Having worked with a wide array of industries including fashion, product marketing, and e-commerce throughout my career, I fully appreciate the importance of true-to-life and visually appealing product photos that accurately represent the actual color of the products. Rest assured that this project will receive my utmost attention to detail and through open communication ensuring final delivery on time and per your precise requirements. Choose me for a product photo enhancer that not only corrects color but also enhances the overall aesthetic quality to truly make your products shine!

@JoseRCarvas

Hi - I build exactly this kind of batch image tool in Python. My approach - estimate the scene illuminant and neutralise the colour cast in one pass using gray-world plus white-patch white balance, with an optional grey reference patch when your shots include one for higher accuracy. I work in linear light and clamp gently so tones stay true to life with no added noise, and the output keeps the original JPEG or PNG resolution. I will add a batch mode so a whole folder of hundreds of images processes in one run, well under five seconds each on a normal desktop, plus a short usage guide and two or three before-and-after samples on generic product shots that show the colour-error drop. Clean, well-commented code so you can extend or embed the function later. To start, send me 3 to 5 representative images and I will return corrected samples for your sign-off before I run the full set. Two quick questions - 1, do your shots sometimes include a grey or white reference card I can lock white balance to, or should it stay fully automatic. 2, is the target colour space standard sRGB for marketplace display. Libraries - Python with OpenCV, NumPy and Pillow. Honest turnaround - the working script with guide and samples in 4 days after we align on a first sample. Thanks - Jose

@UmairBuildsAI

Hey, I can build this color correction tool in Python with OpenCV, handling white balance correction, color cast removal, and batch processing efficiently. You can review my work here: https://www.freelancer.pk/u/UmairBuildsAI My plan for your project: Python script using OpenCV and scikit-image for automatic color cast detection and correction in a single pass. White balance correction using gray world assumption or a reference-based approach depending on accuracy requirements. Batch processing loop handling hundreds of JPEGs or PNGs while retaining original resolution and quality. Processing target under five seconds per 4MP image on a standard desktop. Clean, well-commented code with a usage guide and before-and-after sample outputs demonstrating the improvement. Would you prefer a command-line tool, a simple web interface, or a Python library you can import into your existing pipeline? Happy to jump on a quick call to go through the correction approach before I start. Best Regards, Umair

@arshkumar213

Hi, I can develop a fast, lightweight image color-correction tool using Python and OpenCV to automatically remove color casts, correct white balance, and preserve image quality without introducing artifacts. The solution will support batch processing, retain original resolution, include clean, well-documented code, and provide before/after samples with a simple usage guide. I can deliver within your required timeline.

@shreevee08

Hello, With 4+ years of experience in Python, OpenCV, and image processing, I can develop a lightweight and efficient color-correction tool for product images that delivers natural, consistent results while preserving the original image quality. The solution will include: * Automatic color cast detection and white balance correction * Batch processing for hundreds of JPEG and PNG images * Original resolution preservation * Fast processing with optimized performance * Clean, well-documented Python code * Before-and-after sample images and a simple usage guide My approach focuses on accurate color correction without introducing artifacts or unnecessary noise, making the output suitable for eCommerce marketplaces and product catalogs. I am ready to discuss the correction strategy and deliver a reliable solution within your required timeline. Looking forward to working with you. Best regards,

@davitm12

I propose a lightweight, high-performance Python solution using OpenCV and NumPy to automate true-to-life colour correction. By leveraging the "Gray World" color constancy algorithm, the script will dynamically analyze the R, G, and B channels to detect and eliminate unwanted color casts (such as warm yellow or cool blue tones) in a single, fast pass. The tool will feature full batch processing to handle hundreds of JPEG/PNG images efficiently, maintaining their original resolution without introducing artifacts or digital noise. Utilizing optimized matrix operations, the processing speed will easily benchmark at under 0.2 seconds per 4-megapixel image, completely satisfying your performance requirements. I will provide clean, well-commented code, a quick usage guide, and clear before-and-after visual samples.

@aaminahabiba

Hello, I have carefully reviewed your project and can develop a Python-based solution for automatic product photo colour correction using OpenCV and image processing techniques. My approach would include: • Detecting colour cast and correcting white balance automatically. • Preserving the original image resolution and quality. • Supporting batch processing for JPEG and PNG images. • Writing clean, well-documented, and modular Python code for easy future extension. • Providing a simple usage guide along with before-and-after sample results. I have experience working with Python, OpenCV, image processing and computer vision through AI-based projects involving image preprocessing and analysis. I focus on developing efficient, maintainable solutions that produce reliable results. I am available to start immediately and would be happy to discuss your specific quality requirements and turnaround expectations. I look forward to working with you. Best regards, Amina Habiba

@Ashton4x

Hello, Building a Color-Correcting Product Photo Enhancer sounds simple on the surface — the real challenge is achieving accurate colour correction without compromising image quality. That's exactly the kind of work I enjoy, having prior experience in similar projects. Here's what I'll deliver: • An efficient algorithm in Python using OpenCV for precise colour correction. • Batch processing capability for handling multiple images seamlessly. • User-friendly inputs and outputs while maintaining original image resolution. • Comprehensive usage guide and before-and-after samples showcasing enhanced results. Timeline: Turnaround: 7 days for the complete delivery. One question before I start: How do you envision scaling this tool for potential future enhancements? That'll help me tailor the solution for scalability and easy maintenance. Ready to get started whenever you are.

@umairxdev

Most color correction tools simply shift RGB values, they don't actually remove the underlying color cast. I'd approach this by estimating the scene illuminant first (Gray World/LAB-based correction), then refining white balance while preserving texture and avoiding oversaturation. The result will be a lightweight Python/OpenCV pipeline that batch-processes images in seconds without changing resolution. Before I begin, I'd like to test 3–5 sample images to ensure the correction matches your expected output. If the results satisfy you, we proceed with the full implementation.

@ahmadraza001912

Hi, Colour cast detection in a single pass is the tricky part — most scripts overcorrect highlights or shift neutrals when the source image has mixed lighting. I'd handle it with a grey-world + white-patch hybrid so the correction adapts per image rather than applying a fixed curve. Planned approach: - OpenCV + NumPy for colour cast analysis and correction - scikit-image for perceptual quality checks (ΔE error measurement) - Pillow for JPEG/PNG I/O, retaining original resolution - A simple CLI that handles batch processing across hundreds of images in one go Code will be clean and well-commented so you can embed the function later without reverse-engineering anything. I've built production Python image pipelines before — the kind that need to stay fast on typical hardware, which maps well to your five-second-per-4-megapixel target. One question: do you have sample product photos I can test against, or should I source the before-and-after samples myself?

@SinaAhmadi89

Hello, My approach uses LAB color space for accurate color correction, CLAHE for contrast enhancement, and batch processing for hundreds of images. I will deliver clean, well-commented code with a README guide. Timeline: 5-7 days Budget: 25,000 INR I have experience with similar image processing projects. Best regards, SinaAhmadi89

@RouletteSyst

Hi there, I can develop a lightweight, high-performance Python script using OpenCV and NumPy to handle your batch product photo color-correction needs, ensuring a processing time well under 5 seconds per image. Here is the exact approach and libraries I will use to meet your criteria: 1. Color Cast Detection & White Balance: I will implement the Gray World Algorithm combined with Ground Truth / Perfect Reflection techniques in the LAB color space to isolate and eliminate unnatural color casts without introducing noise or artifacts. 2. Contrast & Tone Correction: I will utilize CLAHE (Contrast Limited Adaptive Histogram Equalization) to balance shadows and highlights, keeping the product true-to-life across different screens and marketplaces. 3. Batch Processing & Resolution Preservation: Using Python's standard 'os' and 'glob' modules alongside OpenCV, the script will batch-process entire folders of JPEGs/PNGs instantly, saving the output while strictly retaining the original resolution, quality, and metadata. 4. Deliverables: You will receive a clean, well-commented .py script (extremely easy to run via terminal/notepad), a brief usage guide, and 3 before-and-after visual samples. I can deliver this within your timeframe. Let's connect so we can discuss the details! Best regards, Christos T.

@abishek129

Hi, this fits well with my CV and image processing background. How I'd build it: Color cast detection: analyze image in LAB color space (perceptually uniform, separates luminance from color) — compute per-channel bias to identify cast type (warm/cool/green/magenta) and magnitude. Correction pipeline: Gray World + White Patch hybrid algorithm for white balance normalization, followed by LAB channel correction to neutralize detected cast. Single-pass, no iterative loops — fast per image. Batch processing: multiprocessing pool across CPU cores — hundreds of images processed in parallel, well under 5 seconds per 4MP image on standard hardware. Input/output: JPEG and PNG in, same format and original resolution out. EXIF metadata preserved. Optional ICC profile support: if source profile is embedded, convert to sRGB for screen/marketplace consistency before correction. Deliverables: well-commented Python script (OpenCV + NumPy + Pillow), CLI with input/output folder args, usage guide, before/after samples on 3 generic product images. Relevant experience: built CV pipelines (YOLOv8, MediaPipe, OpenCV) and image processing workflows for production systems. Timeline: 3-4 days including samples and documentation. Quick question: do your product images typically have a known light source (studio strobe, daylight, mixed retail lighting)? Knowing this lets me tune the white balance algorithm for your specific use case.