CIE XYZ Net: Unprocessing Images for Low-Level Computer Vision Tasks (2006.12709v1)

Abstract: Cameras currently allow access to two image states: (i) a minimally processed linear raw-RGB image state (i.e., raw sensor data) or (ii) a highly-processed nonlinear image state (e.g., sRGB). There are many computer vision tasks that work best with a linear image state, such as image deblurring and image dehazing. Unfortunately, the vast majority of images are saved in the nonlinear image state. Because of this, a number of methods have been proposed to "unprocess" nonlinear images back to a raw-RGB state. However, existing unprocessing methods have a drawback because raw-RGB images are sensor-specific. As a result, it is necessary to know which camera produced the sRGB output and use a method or network tailored for that sensor to properly unprocess it. This paper addresses this limitation by exploiting another camera image state that is not available as an output, but it is available inside the camera pipeline. In particular, cameras apply a colorimetric conversion step to convert the raw-RGB image to a device-independent space based on the CIE XYZ color space before they apply the nonlinear photo-finishing. Leveraging this canonical image state, we propose a deep learning framework, CIE XYZ Net, that can unprocess a nonlinear image back to the canonical CIE XYZ image. This image can then be processed by any low-level computer vision operator and re-rendered back to the nonlinear image. We demonstrate the usefulness of the CIE XYZ Net on several low-level vision tasks and show significant gains that can be obtained by this processing framework. Code and dataset are publicly available at https://github.com/mahmoudnafifi/CIE_XYZ_NET.

• The paper introduces CIE XYZ Net, a deep learning framework that unprocesses sRGB images into a linear, canonical CIE XYZ state, overcoming sensor-dependent limitations.
• It decomposes images into a linear scene-referred component and a residual layer, significantly enhancing low-level tasks like denoising, deblurring, and dehazing.
• The approach enables generalized workflows in computer vision by standardizing image reconstruction, paving the way for unified, device-independent processing.

Overview of "CIE XYZ Net: Unprocessing Images for Low-Level Computer Vision Tasks"

The research paper "CIE XYZ Net: Unprocessing Images for Low-Level Computer Vision Tasks" presents a novel approach to image reconstruction that effectively addresses the limitations associated with existing unprocessing methods. Traditional techniques unprocess nonlinear sRGB images back to raw-RGB states, which are sensor-specific and require camera-specific adjustments. This dependency on device-specific color spaces hampers the application of computer vision tasks like image deblurring, dehazing, and denoising, which benefit from linear color representations. The authors propose utilizing an intermediate, device-independent representation based on the CIE XYZ color space, leveraging it to enhance low-level vision tasks. This canonical image state is not available as output from cameras but is part of the camera processing pipeline.

Methodology

The research introduces a deep learning framework, named CIE XYZ Net, which converts nonlinear image states into CIE XYZ linear image states. The algorithm decomposes sRGB images into two parts: (i) a canonical linear scene-referred image in CIE XYZ color space and (ii) a residual layer reflecting non-linear local photo-finishing operations. This process involves a sequence of learned networks that separate global and local processing operations and maps images between sRGB and CIE XYZ color spaces more efficiently and accurately.

Evaluation and Results

The paper provides extensive validation of CIE XYZ Net on several low-level vision tasks:

• Image Denoising: The method demonstrated clear improvements over traditional methods using datasets such as the smartphone image denoising dataset (SIDD).
• Motion Deblurring: CIE XYZ Net proved advantageous in reducing artifacts and enhancing the sharpness of images compared to sRGB processed images.
• Defocus Blur Detection and Image Dehazing: The approach provides better performance metrics compared to processing directly in sRGB or other linearization approaches such as standard XYZ mapping.
• Raw-RGB Image Reconstruction: The research indicates that reconstructing images in a device-independent CIE XYZ space allows for generalized workflows without dependency on the specific sensor properties, enhancing the flexibility in applications like synthetic raw generation for improving illuminant estimation.

Implications and Future Directions

The implications of standardizing images in the CIE XYZ color space are significant for both practical and theoretical advancements in computer vision. By freeing machine learning models from the constraints of sensor-specific dependencies, this approach allows broader applicability and improved accuracy across diverse imaging devices. It suggests a shift toward more unified frameworks in image processing, potentially simplifying model training and enhancing adaptability.

Future developments may examine the application of CIE XYZ Net to other imaging tasks where linear image characteristics could improve outcomes, including super-resolution tasks and other scene-understanding challenges. Additionally, exploring algorithm efficiency and computational resource management in large-scale deployments will be critical for integration into real-world applications.

In conclusion, CIE XYZ Net marks a substantial contribution to advancing low-level computer vision tasks, providing a versatile and effective framework by leveraging the canonical CIE XYZ color space. The proposed system significantly enhances performance and accuracy compared to traditional sRGB-based processing methods, showcasing strong potential across various domains within computer vision.