Conditional Sequential Modulation for Efficient Global Image Retouching (2009.10390v1)

Abstract: Photo retouching aims at enhancing the aesthetic visual quality of images that suffer from photographic defects such as over/under exposure, poor contrast, inharmonious saturation. Practically, photo retouching can be accomplished by a series of image processing operations. In this paper, we investigate some commonly-used retouching operations and mathematically find that these pixel-independent operations can be approximated or formulated by multi-layer perceptrons (MLPs). Based on this analysis, we propose an extremely light-weight framework - Conditional Sequential Retouching Network (CSRNet) - for efficient global image retouching. CSRNet consists of a base network and a condition network. The base network acts like an MLP that processes each pixel independently and the condition network extracts the global features of the input image to generate a condition vector. To realize retouching operations, we modulate the intermediate features using Global Feature Modulation (GFM), of which the parameters are transformed by condition vector. Benefiting from the utilization of $1\times1$ convolution, CSRNet only contains less than 37k trainable parameters, which is orders of magnitude smaller than existing learning-based methods. Extensive experiments show that our method achieves state-of-the-art performance on the benchmark MIT-Adobe FiveK dataset quantitively and qualitatively. Code is available at https://github.com/hejingwenhejingwen/CSRNet.

• The paper presents CSRNet, a conditional sequential modulation framework that drastically reduces trainable parameters while performing high-quality global image retouching.
• CSRNet leverages a base network using 1×1 convolutions for pixel-level adjustments and a condition network to modulate features based on global image cues.
• Quantitative tests on the MIT-Adobe FiveK dataset show state-of-the-art performance with fewer than 37k parameters, outperforming larger models.

Conditional Sequential Modulation for Efficient Global Image Retouching

The paper "Conditional Sequential Modulation for Efficient Global Image Retouching" proposes Conditional Sequential Retouching Network (CSRNet), a novel framework designed for global photo retouching, addressing common photographic defects like incorrect exposure and poor contrast. CSRNet leverages mathematical insights from image processing operations approximated by multi-layer perceptrons (MLPs) to facilitate efficient image enhancement. The proposed network demonstrates a significant reduction in trainable parameters compared to existing methods, showcasing its lightweight architecture particularly suited for practical applications in consumer electronics like smartphones and photo editing software.

CSRNet is composed of two main components: the base network and the condition network. The base network operates similarly to an MLP, treating each pixel independently by means of $1\times1$ convolutions, effectively handling pixel-independent retouching operations such as brightness and contrast adjustments. The condition network extracts global image features to generate a condition vector, which modulates intermediate features of the base network through Global Feature Modulation (GFM). This structure supports a sequential processing model, enabling CSRNet to perform image retouching without the need for extensive computational resources.

Quantitative evaluations on the MIT-Adobe FiveK dataset demonstrate that CSRNet achieves state-of-the-art results, outperforming existing approaches both in terms of performance quality and computational efficiency. CSRNet utilizes less than 37k parameters, a significant reduction compared to methods like HDRNet (482k parameters) and DPE (over 3M parameters), underlining its efficient design. The paper further emphasizes CSRNet's capability to adjust retouching styles by fine-tuning only the condition network, offering flexibility in meeting diverse user preferences.

The findings position CSRNet as a powerful tool in automatic photo retouching, offering a balance between accuracy and efficiency. By integrating a simplified architecture with minimal parameters, CSRNet maintains high-quality image retouching, indicating its suitability for low-resource environments and real-time applications. The paper also opens avenues for future research on adaptive neural network designs, potentially leading to advancements in AI-driven image processing techniques with broader applications beyond global image retouching.