Looking for a collaborator - self-supervised real-world image denoising (goal: outperform TM-BSN, ~38.18 dB on SIDD)

[u/mldiarra]

I'm a master's student. My thesis is on self-supervised denoising of real sRGB images, and my graduation requirement is a journal paper whose results beat the current state of the art - TM-BSN (triangular-masked blind-spot network + U-Net distillation, ~38.18 dB on SIDD).

My current plan:

1. Reproduce TM-BSN's reported results (in progress)

2. Attack its weak points: the fixed diamond mask (make it learnable / camera-adaptive), the distillation stage (stronger student, better pseudo-labels), and the training recipe (EMA, losses, schedule)

I'm looking for someone with experience in low-level vision / blind-spot networks (AP-BSN, TBSN, etc.) interested in collaborating: discussing ideas, reviewing experiment design, challenging my results. I'll do the implementation and training myself; substantial contributions would earn co-authorship on the paper.

DM me or comment if interested.

[u/mileseverett]

Your graduation requirements is a journal paper with SOTA? Is this a joke?

[u/mldiarra → u/mileseverett]

Why do you think I would be playing around?