7 E-commerce Image Processing Workflow Fixes

Image processing for e-commerce usually is regarded as the following series of actions: background removal, color correction, cropping, and exporting the image. In reality, there is quite a substantial difference between an unprocessed image and the image of the product that was properly processed. Edge contamination, color inconsistency, improper scaling, dust and dirt, reflection, noise, and wrong shadows can be left after the basic image processing.

The choice of the technique is important as well. Manual retouching allows for full control over the process but is hard to automate for a catalog with hundreds or even thousands of images. The processing with AI allows to make repetitive tasks quickly, but segmentation errors, transparent surface, and incorrect product reconstruction require verification anyway.

1. Define the Distinction between Raw and Retouched Photographs

Raw product photography is likely to have issues like irregular lighting, dust on the image, background gradients, perspective problems, color shifts, and extraneous objects surrounding the product. Increasing the brightness or contrast levels cannot fix these structural issues.

In professional photo retouching, an image is processed separately on different technical aspects. The stages include isolating the background, cleaning up the image, tonal adjustments, color corrections, shadows, sharpness, and cropping of the image. Doing these corrections separately avoids creating other issues through these corrections.

For instance, correcting the global contrast of a flat product is likely to increase the dark shadows of the background of the picture. By doing separate corrections, one can avoid such problems.

2. Perform Manual Retouching for Cases Where Accuracy Is Essential

The process of manual retouching is still important for products whose contours are complex and require very high precision. Jewelry, transparent packaging, shiny metal, delicate fabric, mesh, fur, and products with thin cables may create problems with segmentation that cannot be solved with one automatic mask.

With a manual approach, it is possible to check the contours of the alpha channel under a microscope, fix edge halos, preserve partial transparency, and build clipping paths if needed. It is also possible to consider shadows and reflections as different elements from the background image, not removing them accidentally.

The disadvantage is scalability. Performing all these actions manually on thousands of similar pictures takes much more time and leaves the possibility of inconsistent decisions from picture to picture.

3. Using AI Process for Repeatable Image Manipulation

AI-powered image processing is highly useful when one needs to repeat some corrections on a large amount of somewhat predictable product photos. The operations like background segmentation, object recognition, dust removal, basic corrections of exposure, white balance, cropping, centering, and basic assessment of image quality could be done automatically.

Modern product photos processing pipeline can assess attributes like sharpness, exposure, composition, and presence of duplicated frames prior to sending photos for additional processing. In this way, AI could serve not just as a tool for editing, but also as a filter and quality control.

Still, automatic processing cannot be considered as the guarantee of accuracy. Studies of the automated generation of product backgrounds have indicated product consistency and incorrect generated backgrounds as quality problems.

4. Review Hard-to-Segment Edges after Automated Segmentation

There is no clearer example of when AI differs from manual processing than when removing background. The segmentation model can detect the major product area very fast, but hard-to-segment edges can consist of transparent pixels, fine strands, reflections, or colored contamination by the background.

A good practice after segmentation is to inspect the mask and not take the first output for granted. During the edge inspection, you will have to pay attention to halos, missing product pixels, excessive blurring, damaged fine elements, and leftovers of the background.

The rigid product doesn't need much manual intervention after the automatic mask generation. Jewelry, apparel, glass objects, or products with fine elements should be carefully refined manually.

5. Distinct Product Accuracy from Visual Enhancement

The process of AI enhancement may sharpen the image, eliminate noise, or adjust its exposure; however, enhancement becomes a problem when it alters the appearance of the physical product. The accuracy of the color, texture, geometry, logos, seams, patterns, and relations between elements in the object must be preserved.

In this sense, the distinction becomes especially relevant in fashion, jewelry, electronics, and other categories, where even small visual discrepancies may change the interpretation of the product by the buyer. For this reason, visual enhancement should be performed within the defined range of corrections.

6. Using Human Review as a Technical Quality Gate

In a hybrid process, the idea is not to manually analyze all the pixels in each image. Instead, human review may be inserted into that stage where automated processing is most likely to generate an undesirable output.

The image processing pipeline can detect issues such as ambiguous segmentation, irregular sizes, lack of sharpness, color discrepancy, no product in the image, and non-uniform positioning. In such cases, the editor can fix those images while letting other images pass through the automatic process.

This establishes a process wherein AI deals with repetitive tasks, human quality control discovers the exception, and human retouching fixes the problem. This kind of process is being used already in other image processing processes where there is automated editing followed by human approval.

7. Create a Unified AI and Manual Workflow

The new paradigm thus does not boil down to the opposition of AI and manual photo manipulation. A more relevant scenario implies that the two approaches work together to perform the tasks they excel in best.

The basic workflow starts with the image acquisition and its quality screening. Then follows AI-powered segmentation, background retouching, initial color and exposure adjustment, cropping, and proper placement. The automated rule-based quality check helps to catch uncertain or erroneous cases. Manual photo editing works on complicated edges, reflections, special flaws, color-sensitive spots, and all other exceptional cases. The final step involves verifying the dimensions, color consistency, background editing, shadows, and export quality.

Thus, photo editing is transformed from a set of independent actions into a systematic process of image processing. It is now possible to ensure uniform retouching of an entire photo gallery without obliging all images to undergo a manual editing process.

Create a More Dependable Image Processing Workflow for Your E-commerce Business

This choice is not just about choosing faster or higher quality images since each type of image processing has its own technical strengths and weaknesses.

Unedited images allow you to keep the original image but fail to solve the visual problem. Manual editing gives you maximum control but entails lots of monotonous labor. Automated image processing is efficient when dealing with routine tasks but fails at complex visual edges and product-specific precision. Combined AI and manual editing ensure automated processing and subsequent human adjustments to guarantee the highest level of product accuracy.

Share a representative sample of your product images that have irregular backgrounds, color inaccuracies, complex edges, unnecessary reflections, shadows of inconsistent darkness, and large volumes of repetitive corrections needed, and our retouching team will find out what can be processed automatically and what should be edited manually.