DEALing with Image Reconstruction: Deep Attentive Least Squares
Authors: Mehrsa Pourya, Erich Kobler, Michael Unser, Sebastian Neumayer
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental evaluations show results on par with state-of-the-art methods for various inverse problems. In Section 5, Experiments are conducted, including 'Grayscale and Color Denoising', 'Color Superresolution', and 'MRI Reconstruction', presenting results in tables (e.g., Table 1, 2, 3, 5) and figures (e.g., Figure 2, 4, 8, 9), discussing metrics like PSNR and SSIM, and using 'Dataset and Loss' for training. |
| Researcher Affiliation | Academia | 1Biomedical Imaging group, EPFL Lausanne, Switzerland 2Institute for Machine Learning, JKU Linz, Austria 3Faculty of Mathematics, TU Chemnitz, Germany. |
| Pseudocode | No | The paper describes its methodology in Section 3, 'Methodology', using prose, equations, and architectural diagrams (e.g., Figure 1). There are no explicitly labeled pseudocode or algorithm blocks, nor any structured procedures formatted like code. |
| Open Source Code | Yes | Code: https://github.com/mehrsapo/DEAL. |
| Open Datasets | Yes | As training set D = {xm}M m=1, we use the images proposed in Zhang et al. (2022). ... We provide in Table 1 the average peak signal-to-noise ratios (PSNR) achieved by various methods over the images of the BSD68 set and the CBSD68 set ... knee images from the fast MRI dataset (Knoll et al., 2020) |
| Dataset Splits | Yes | As training set D = {xm}M m=1, we use the images proposed in Zhang et al. (2022). ... To estimate the parameters θ from the training data, we use the loss ... At each step of the optimizer, we sample 16 patches of size (128 128) randomly from D. ... We use the set3 and set12 datasets to validate the color and grayscale models, respectively. |
| Hardware Specification | Yes | We report the computation times for several methods on a Tesla V100-SXM2-32GB GPU. ... We perform our experiments on a Tesla V100-SXM2-32 GB GPU. |
| Software Dependencies | No | The paper mentions using 'Adam (Kingma & Ba, 2015)' as an optimizer. However, it does not provide specific software library names with version numbers (e.g., PyTorch 1.9, TensorFlow 2.x, Python 3.8). |
| Experiment Setup | Yes | We set ϵout = ϵin = 1 10 4 and limit the number of CG steps to Kin = 50. ... First, we train the gray and color models for 70 000 and 40 000 steps, respectively, with an initial learning rate of 5 10 4 that is reduced to 4 10 4 by a cosine annealing scheduler. Then, we continue the training of the gray and color model for 10 000 and 5000 steps, respectively, with an initial learning rate of 2 10 4 that is reduced to 1 10 7 by annealing. ... To promote convergence of (6) to a fixed point, we sample Kout uniformly from [15, 60] ... We minimize the loss (12) ... with γ = 10 4. |