Model-Based Image Signal Processors via Learnable Dictionaries

• URL: http://arxiv.org/abs/2201.03210v1
• Date: Mon, 10 Jan 2022 08:36:10 GMT
• Title: Model-Based Image Signal Processors via Learnable Dictionaries
• Authors: Marcos V. Conde, Steven McDonagh, Matteo Maggioni, Ale\v{s} Leonardis, Eduardo P\'erez-Pellitero
• Abstract summary: Digital cameras transform sensor RAW readings into RGB images by means of their Image Signal Processor (ISP) Recent approaches have attempted to bridge this gap by estimating the RGB to RAW mapping. We present a novel hybrid model-based and data-driven ISP that is both learnable and interpretable.
• Score: 6.766416093990318
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Digital cameras transform sensor RAW readings into RGB images by means of their Image Signal Processor (ISP). Computational photography tasks such as image denoising and colour constancy are commonly performed in the RAW domain, in part due to the inherent hardware design, but also due to the appealing simplicity of noise statistics that result from the direct sensor readings. Despite this, the availability of RAW images is limited in comparison with the abundance and diversity of available RGB data. Recent approaches have attempted to bridge this gap by estimating the RGB to RAW mapping: handcrafted model-based methods that are interpretable and controllable usually require manual parameter fine-tuning, while end-to-end learnable neural networks require large amounts of training data, at times with complex training procedures, and generally lack interpretability and parametric control. Towards addressing these existing limitations, we present a novel hybrid model-based and data-driven ISP that builds on canonical ISP operations and is both learnable and interpretable. Our proposed invertible model, capable of bidirectional mapping between RAW and RGB domains, employs end-to-end learning of rich parameter representations, i.e. dictionaries, that are free from direct parametric supervision and additionally enable simple and plausible data augmentation. We evidence the value of our data generation process by extensive experiments under both RAW image reconstruction and RAW image denoising tasks, obtaining state-of-the-art performance in both. Additionally, we show that our ISP can learn meaningful mappings from few data samples, and that denoising models trained with our dictionary-based data augmentation are competitive despite having only few or zero ground-truth labels.

Related papers

• A Learnable Color Correction Matrix for RAW Reconstruction [19.394856071610604]
  We introduce a learnable color correction matrix (CCM) to approximate the complex inverse image signal processor (ISP) Experimental results demonstrate that simulated RAW (simRAW) images generated by our method provide performance improvements equivalent to those produced by more complex inverse ISP methods.
  arXiv  Detail & Related papers  (2024-09-04T07:46:42Z)
• RAW-Adapter: Adapting Pre-trained Visual Model to Camera RAW Images [51.68432586065828]
  We introduce RAW-Adapter, a novel approach aimed at adapting sRGB pre-trained models to camera RAW data. Raw-Adapter comprises input-level adapters that employ learnable ISP stages to adjust RAW inputs, as well as model-level adapters to build connections between ISP stages and subsequent high-level networks.
  arXiv  Detail & Related papers  (2024-08-27T06:14:54Z)
• Simple Image Signal Processing using Global Context Guidance [56.41827271721955]
  Deep learning-based ISPs aim to transform RAW images into DSLR-like RGB images using deep neural networks. We propose a novel module that can be integrated into any neural ISP to capture the global context information from the full RAW images. Our model achieves state-of-the-art results on different benchmarks using diverse and real smartphone images.
  arXiv  Detail & Related papers  (2024-04-17T17:11:47Z)
• Rawformer: Unpaired Raw-to-Raw Translation for Learnable Camera ISPs [53.68932498994655]
  This paper introduces a novel method for unpaired learning of raw-to-raw translation across diverse cameras. It accurately maps raw images captured by a certain camera to the target camera, facilitating the generalization of learnable ISPs to new unseen cameras. Our method demonstrates superior performance on real camera datasets, achieving higher accuracy compared to previous state-of-the-art techniques.
  arXiv  Detail & Related papers  (2024-04-16T16:17:48Z)
• Beyond Learned Metadata-based Raw Image Reconstruction [86.1667769209103]
  Raw images have distinct advantages over sRGB images, e.g., linearity and fine-grained quantization levels. They are not widely adopted by general users due to their substantial storage requirements. We propose a novel framework that learns a compact representation in the latent space, serving as metadata.
  arXiv  Detail & Related papers  (2023-06-21T06:59:07Z)
• Self-Supervised Reversed Image Signal Processing via Reference-Guided Dynamic Parameter Selection [1.1602089225841632]
  We propose a self-supervised reversed ISP method that does not require metadata and paired images. The proposed method converts a RGB image into a RAW-like image taken in the same environment with the same sensor as a reference RAW image. We show that the proposed method is able to learn various reversed ISPs with comparable accuracy to other state-of-the-art supervised methods.
  arXiv  Detail & Related papers  (2023-03-24T11:12:05Z)
• Efficient Visual Computing with Camera RAW Snapshots [41.9863557302409]
  Conventional cameras capture image irradiance on a sensor and convert it to RGB images using an image signal processor (ISP) One can argue that since RAW images contain all the captured information, the conversion of RAW to RGB using an ISP is not necessary for visual computing. We propose a novel $rho$-Vision framework to perform high-level semantic understanding and low-level compression using RAW images.
  arXiv  Detail & Related papers  (2022-12-15T12:54:21Z)
• Reversed Image Signal Processing and RAW Reconstruction. AIM 2022 Challenge Report [109.2135194765743]
  This paper introduces the AIM 2022 Challenge on Reversed Image Signal Processing and RAW Reconstruction. We aim to recover raw sensor images from the corresponding RGBs without metadata and, by doing this, "reverse" the ISP transformation.
  arXiv  Detail & Related papers  (2022-10-20T10:43:53Z)
• Enabling ISP-less Low-Power Computer Vision [4.102254385058941]
  We release the raw version of a large-scale benchmark for generic high-level vision tasks. For ISP-less CV systems, training on raw images result in a 7.1% increase in test accuracy. We propose an energy-efficient form of analog in-pixel demosaicing that may be coupled with in-pixel CNN computations.
  arXiv  Detail & Related papers  (2022-10-11T13:47:30Z)
• Transform your Smartphone into a DSLR Camera: Learning the ISP in the Wild [159.71025525493354]
  We propose a trainable Image Signal Processing framework that produces DSLR quality images given RAW images captured by a smartphone. To address the color misalignments between training image pairs, we employ a color-conditional ISP network and optimize a novel parametric color mapping between each input RAW and reference DSLR image.
  arXiv  Detail & Related papers  (2022-03-20T20:13:59Z)
• ISP Distillation [38.19032198060534]
  High-level machine vision models, such as object recognition or semantic segmentation, assume images are transformed into some canonical image space by the camera. The camera ISP is optimized for producing visually pleasing images for human observers and not for machines. We show that our performance on RAW images for object classification and semantic segmentation is significantly better than models trained on labeled RAW images.
  arXiv  Detail & Related papers  (2021-01-25T16:12:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.