Pruning Overparameterized Multi-Task Networks for Degraded Web Image
  Restoration
Image quality is a critical factor in delivering visually appealing content
on web platforms. However, images often suffer from degradation due to lossy
operations applied by online social networks (OSNs), negatively affecting user
experience. Image restoration is the process of recovering a clean high-quality
image from a given degraded input. Recently, multi-task (all-in-one) image
restoration models have gained significant attention, due to their ability to
simultaneously handle different types of image degradations. However, these
models often come with an excessively high number of trainable parameters,
making them computationally inefficient. In this paper, we propose a strategy
for compressing multi-task image restoration models. We aim to discover highly
sparse subnetworks within overparameterized deep models that can match or even
surpass the performance of their dense counterparts. The proposed model, namely
MIR-L, utilizes an iterative pruning strategy that removes low-magnitude
weights across multiple rounds, while resetting the remaining weights to their
original initialization. This iterative process is important for the multi-task
image restoration model's optimization, effectively uncovering "winning
tickets" that maintain or exceed state-of-the-art performance at high sparsity
levels. Experimental evaluation on benchmark datasets for the deraining,
dehazing, and denoising tasks shows that MIR-L retains only 10% of the
trainable parameters while maintaining high image restoration performance. Our
code, datasets and pre-trained models are made publicly available at
https://github.com/Thomkat/MIR-L.
68f621a18589920bf4d322c8
Thomas Katraouras
claimed_verified
2025-10-20T12:09:22.432Z
6676179e4b1e661916d0c654
/avatars/a074b2c7baa49de9324329c752b49dfd.svg
Thomas Katraouras
user
Tomk187
68f621a18589920bf4d322c9
Dimitrios Rafailidis
Pruning Overparameterized Multi-Task Networks for Degraded Web Image
  Restoration
Image quality is a critical factor in delivering visually appealing content
on web platforms. However, images often suffer from degradation due to lossy
operations applied by online social networks (OSNs), negatively affecting user
experience. Image restoration is the process of recovering a clean high-quality
image from a given degraded input. Recently, multi-task (all-in-one) image
restoration models have gained significant attention, due to their ability to
simultaneously handle different types of image degradations. However, these
models often come with an excessively high number of trainable parameters,
making them computationally inefficient. In this paper, we propose a strategy
for compressing multi-task image restoration models. We aim to discover highly
sparse subnetworks within overparameterized deep models that can match or even
surpass the performance of their dense counterparts. The proposed model, namely
MIR-L, utilizes an iterative pruning strategy that removes low-magnitude
weights across multiple rounds, while resetting the remaining weights to their
original initialization. This iterative process is important for the multi-task
image restoration model's optimization, effectively uncovering "winning
tickets" that maintain or exceed state-of-the-art performance at high sparsity
levels. Experimental evaluation on benchmark datasets for the deraining,
dehazing, and denoising tasks shows that MIR-L retains only 10% of the
trainable parameters while maintaining high image restoration performance. Our
code, datasets and pre-trained models are made publicly available at
https://github.com/Thomkat/MIR-L.
MIR-L, a compressed multi-task image restoration model, achieves high performance with significantly reduced parameters through iterative pruning and weight resetting.
multi-task image restoration overparameterized deep models sparse subnetworks iterative pruning low-magnitude weights winning tickets deraining dehazing denoising